machinelearning_confusionmatrix.py

# -*- coding: utf-8 -*-
"""MachineLearning_ConfusionMatrix.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1z0lZldXvY4rw2b9WEzZqvIaN7FHRAVIq
"""

import matplotlib.pyplot as plt
import numpy
from sklearn import metrics
#generating random data
actual = numpy.random.binomial(1,.9,size = 1000)
predicted = numpy.random.binomial(1,.9,size = 1000)
#function call
confusion_matrix = metrics.confusion_matrix(actual, predicted)

cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])

cm_display.plot()
plt.show()
'''
False Negative (Top-Left Quadrant)
False Positive (Top-Right Quadrant)
True Negative (Bottom-Left Quadrant)
True Positive (Bottom-Right Quadrant)
'''
#Accuracy
#(True Positive + True Negative) / Total Predictions
Accuracy = metrics.accuracy_score(actual, predicted)
#Precision
#True Positive / (True Positive + False Positive)
Precision = metrics.precision_score(actual, predicted)
#Sensitivity
#True Positive / (True Positive + False Negative)
Sensitivity_recall = metrics.recall_score(actual, predicted)
#Specificity
#True Negative / (True Negative + False Positive)
Specificity = metrics.recall_score(actual, predicted, pos_label=0)
#F-score
# 2 * ((Precision * Sensitivity) / (Precision + Sensitivity))
F1_score = metrics.f1_score(actual, predicted)

#metrics
print({"Accuracy":Accuracy,"Precision":Precision,"Sensitivity_recall":Sensitivity_recall,"Specificity":Specificity,"F1_score":F1_score})